摘要
为了提高教学质量评价的准确性,提出一种基于灰色理论和BP神经网络相融合的教学质量评价方法,研究首先确定对评价结果有重要贡献率的13个指标作为灰色神经网络输入向量,然后在BP神经网络中引入灰色模型构建灰色神经网络模型,最后通过发放问卷的形式收集课程的30个样本数据并训练灰色神经网络,利用训练后的神经网络评价测试样本及数据。结果表明:灰色理论的神经网络模型误差均方差是0.92,小于BP神经网络模型,可以有效评价课程的教学质量。
To improve the accuracy of the method for evaluating teaching quality, an evaluation method of teaching quality based on grey theory and BP neural network was proposed in this paper. At first, 13 evaluation indicators with an important contribution for evaluation results were determined and used as a grey neural network input vector. And then the grey model was introduced into BP neural network to build the grey neural network model. Finally, 30 sample data for the courses were collected through the form of a questionnaire, and used to train the grey neural network, and then the sample data were evaluated and tested using the trained grey neural network. The results showed that the standard deviation of grey neural network model was 0.92, which was less than that of the BP neural network model. The result indicates that the grey neural network model can effectively evaluate the teaching quality of the courses.
出处
《黑龙江畜牧兽医》
CAS
北大核心
2014年第8期36-39,共4页
Heilongjiang Animal Science And veterinary Medicine
基金
吉林省教育科学"十二五"规划课题(GH13181)
吉林农业大学教育教学研究课题
关键词
神经网络
灰色理论
教学质量
评价模型
评估指标
neural network
grey theory
teaching quality
evaluation model
evaluation index
